Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Automatica (Journal of IFAC)
Robust and optimal control
Kernel independent component analysis
The Journal of Machine Learning Research
On Consistency of Subspace Methods for System Identification
Automatica (Journal of IFAC)
Subspace identification of multivariable linear parameter-varying systems
Automatica (Journal of IFAC)
Analysis of the asymptotic properties of the MOESP type of subspace algorithms
Automatica (Journal of IFAC)
LPV control and full block multipliers
Automatica (Journal of IFAC)
On the ill-conditioning of subspace identification with inputs
Automatica (Journal of IFAC)
Subspace identification of Bilinear and LPV systems for open- and closed-loop data
Automatica (Journal of IFAC)
Asymptotically optimal orthonormal basis functions for LPV system identification
Automatica (Journal of IFAC)
Black-box performance models for virtualized web service applications
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Expert Systems with Applications: An International Journal
Identification and predictive control for a circulation fluidized bed boiler
Knowledge-Based Systems
A convex relaxation approach to set-membership identification of LPV systems
Automatica (Journal of IFAC)
Environmental Modelling & Software
Hi-index | 22.15 |
A novel subspace identification method is presented which is able to reconstruct the deterministic part of a multivariable state-space LPV system with affine parameter dependence, in the presence of process and output noise. It is assumed that the identification data is generated with the scheduling variable varying periodically during the course of the identification experiment. This allows to use methods from LTI subspace identification to determine the column space of the time-varying observability matrices. It is shown that the crucial step in determining the original LPV system is to ensure the obtained observability matrices are defined with respect to the same state basis. Once the LPV model has been identified, it is valid for other nonperiodic scheduling sequences as well.